Network characterization of the Entangled Model for sustainability indicators. Analysis of the network properties for scenarios

Assessing Global Environmental Sustainability Via an Unsupervised Clustering Framework

The importance of sustainable development has risen in recent years due to the significant number of people affected by lack of access to essential resources as well as the need to prepare for and adapt to intensifying climate change and rapid urbanization. Modeling frameworks capable of effectively assessing and tracking sustainability lie at the heart of creating effective policies to address these issues. Conventional frameworks, such as the Environmental Performance Index (EPI), that support such policies often involve ranking countries based on a weighted sum of a number of relevant environmental metrics. However, the selection and weighing processes are often biased. Moreover, the ranking process fails to provide policymakers with possible avenues to improve their country’s environmental sustainability. This study aimed to address these gaps by proposing a novel data-driven framework to assess the environmental sustainability of countries objectively by leveraging unsupervised learning theory. Specifically, this framework harnesses a clustering technique known as Self-Organized Maps to group countries based on their characteristic environmental performance metrics and track progression in terms of shifts within clusters over time. The results support the hypothesis that the inconsistencies in the EPI calculation can lead to misrepresentations of the relative sustainability of countries over time. The proposed framework, which does not rely on ranking or data transformations, enables countries to make more informed decisions by identifying effective and specific pathways towards improving their environmental sustainability.

A socio-ecological approach to improve industrial zones towards eco-industrial parks

One of the concrete examples of industrial symbiosis development is eco-industrial parks, which improves resource efficiency and minimizes environmental impacts by adopting models for waste exchanges between industries. Despite past efforts, many industrial zones around the world are not yet considered as eco-industrial parks because of the low number (or total lack) of symbiotic relationships among industries. A promising strategy is to develop those existing industrial zones into eco-industrial parks. However, there is a lack of studies addressing how to assess environmental improvement in relation to network sustainability. This study demonstrates such an assessment approach using an integration of food web analysis and social network analysis. These two methods can assist in assessing differences in network configurations with respect to potential implementations of industrial symbiosis, and in analysing the resilience, redundancy, connectance, and cyclicity of eco-parks. The use of the methods is illustrated in a case study of an industrial zone in Turkey. Four potential future scenarios are proposed, including potential future co-location of companies in the industrial zone in order to foster industrial symbiotic network formation. These scenarios are compared with the current configuration. The results indicate the method's ability to assess the resilience of an industrial network. Moreover, the case shows an improvement of network sustainability and follows some sustainable properties of natural ecosystems as a result of implementing the industrial symbiosis.